Machine Learning Engineer

July 28

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Description

• Conceptualize, develop, and deploy machine learning models that underpin our NLP, retrieval, ranking, reasoning, dialog and code-generation systems. • Implement advanced machine learning algorithms, such as Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems to continually improve the performance of our AI systems. • Lead the processing and analysis of large, complex datasets (structured, semi-structured, and unstructured), and use your findings to inform the development of our models. • Work across the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment. • Implement A/B testing and other statistical methods to validate the effectiveness of models. Ensure the integrity and robustness of ML solutions by developing automated testing and validation processes. • Clearly communicate the technical workings and benefits of ML models to both technical and non-technical stakeholders, facilitating understanding and adoption.

Requirements

• A Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field. • Proven industry experience in building and deploying production-level machine learning models. • Deep understanding and practical experience with NLP techniques and frameworks, including training and inference of large language models. • Deep understanding of any of retrieval, ranking, reinforcement learning, and agent-based systems and experience in how to build them for large systems. • Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch. • Excellent skills in data processing (SQL, ETL, data warehousing) and experience working with large-scale data systems. • Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practices. • Familiarity with cloud platforms like GCP or Azure. • Familiarity with the latest industry and academic trends in machine learning and AI, and the ability to apply this knowledge to practical projects. • Good understanding of software development principles, data structures, and algorithms. • Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking. • The ability to work collaboratively in an extremely fast-paced, startup environment.